GESIS Training Courses

Scientific Coordination

Alisa Remizova

Administrative Coordination

Noemi Hartung

Introduction to Methods of Causal Inference

Online via Zoom
Additional links
Lecturer(s): Michael Gebel

About the lecturer - Michael Gebel

Course description

Please note that this workshop will be held on 05-06 December and 12-13 December! Please go to the schedule for more details.  
Alongside description and prediction, causal inference is one of the central aims of quantitative empirical social research. In research practice, often only non-experimental data are available, which makes causal inference difficult due to non-random selection. This workshop provides a basic introduction to non-experimental methods of causal inference that explicitly address the problem of non-random selection and are increasingly used in social science research. As an alternative to workshops that specialize in one method, this workshop offers an introductory general overview of different methods of causal inference. Firstly, the counterfactual model and directed acyclic graphs (DAGs) are introduced as overarching theoretical frameworks and practiced using examples. The implications for regression analysis with regard to the selection of control variables and model building are explained. Building on this, the methods of regression adjustment, propensity score matching, (coarsened) exact matching, entropy balancing, inverse probability weighting, instrument variable estimators, regression discontinuity design, and difference-of-differences estimators are presented in an application-oriented introduction. The methods are practiced with the statistics program Stata using data and simple examples from social sciences research.
Organizational structure of the course
The course is taught live (no recordings) via Zoom. The course is divided into input and exercise units of equal duration. The input units will be lecture-based, with opportunities for Q&A and discussion. During the exercise units on the counterfactual model and causal graphs, participants will be divided into groups to solve small problems together, and the solutions will be jointly discussed. During the hands-on empirical exercises, participants will learn how to implement the different methods of causal inference in simple empirical exercises using the statistical program Stata and simple data and examples from social science research. Joint discussions will focus on the proper application of each method and interpretation of the results. Given the introductory and overview character of the course and the expected heterogeneity of participants, it is not intended that participants work on their own research examples. However, participants should be able to apply the knowledge gained to their own research after the course.

Target group

Participants will find the course useful if:
They are doctoral students and scientists in the field of social sciences who like to get an application-oriented introductory overview of the foundations and methods of causal inference.

Learning objectives

By the end of the course, participants will be able to:
  • explain the basic concepts of causal inference,
  • explain the logic and assumptions of different methods of causal inference,
  • select appropriate methods of causal inference according to the pre-specified research hypothesis and available data,
  • and independently apply methods of causal inference using Stata and understand and interpret the results of the analyses (Stata output).


  • Good knowledge of multiple linear and logistic regression
  • Basic knowledge of Stata
    Software requirements
    Participants using their own laptops will need Stata (version 15 or later) with ados psmatch2, teffects, kmatch, moremata, kdens, and cem installed. If you need Stata, please let us know the latest two weeks in advance of the course start so that we can take care of it.


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